R version 2.4.0 (2006-10-03)
Copyright (C) 2006 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign("nameEx", 
+        local({
+ 	   s <- "__{must remake R-ex/*.R}__"
+            function(new) {
+                if(!missing(new)) s <<- new else s
+            }
+        }),
+        pos = "CheckExEnv")
> ## Add some hooks to label plot pages for base and grid graphics
> assign("base_plot_hook",
+        function() {
+            pp <- par(c("mfg","mfcol","oma","mar"))
+            if(all(pp$mfg[1:2] == c(1, pp$mfcol[2]))) {
+                outer <- (oma4 <- pp$oma[4]) > 0; mar4 <- pp$mar[4]
+                mtext(sprintf("help(\"%s\")", nameEx()), side = 4,
+                      line = if(outer)max(1, oma4 - 1) else min(1, mar4 - 1),
+               outer = outer, adj = 1, cex = .8, col = "orchid", las=3)
+            }
+        },
+        pos = "CheckExEnv")
> assign("grid_plot_hook",
+        function() {
+            pushViewport(viewport(width=unit(1, "npc") - unit(1, "lines"),
+                                  x=0, just="left"))
+            grid.text(sprintf("help(\"%s\")", nameEx()),
+                      x=unit(1, "npc") + unit(0.5, "lines"),
+                      y=unit(0.8, "npc"), rot=90,
+                      gp=gpar(col="orchid"))
+        },
+        pos = "CheckExEnv")
> setHook("plot.new",     get("base_plot_hook", pos = "CheckExEnv"))
> setHook("persp",        get("base_plot_hook", pos = "CheckExEnv"))
> setHook("grid.newpage", get("grid_plot_hook", pos = "CheckExEnv"))
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   .CheckExEnv <- as.environment("CheckExEnv")
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        pos = "CheckExEnv")
> assign("ptime", proc.time(), pos = "CheckExEnv")
> grDevices::postscript("multcomp-Ex.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), pos = "CheckExEnv")
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('multcomp')
Loading required package: mvtnorm
> 
> assign(".oldSearch", search(), pos = 'CheckExEnv')
> assign(".oldNS", loadedNamespaces(), pos = 'CheckExEnv')
> cleanEx(); nameEx("cholesterol");
> ### * cholesterol
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: cholesterol
> ### Title: Cholesterol Reduction Data Set
> ### Aliases: cholesterol
> ### Keywords: datasets
> 
> ### ** Examples
> 
> 
>   ### adjusted p-values for all-pairwise comparisons in a one-way layout 
>   ### set up ANOVA model  
>   amod <- aov(response ~ trt, data = cholesterol)
> 
>   ### set up multiple comparisons object for all-pair comparisons
>   cht <- glht(amod, linfct = mcp(trt = "Tukey"))
> 
>   ### cf. Westfall et al. (1999, page 171)
>   summary(cht, test = univariate())

	 Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: aov(formula = response ~ trt, data = cholesterol)

Linear Hypotheses:
                     Estimate Std. Error t value  p value    
2times - 1time == 0     3.443      1.443   2.385 0.021333 *  
4times - 1time == 0     6.593      1.443   4.568 3.82e-05 ***
drugD - 1time == 0      9.579      1.443   6.637 3.53e-08 ***
drugE - 1time == 0     15.166      1.443  10.507 1.08e-13 ***
4times - 2times == 0    3.150      1.443   2.182 0.034352 *  
drugD - 2times == 0     6.136      1.443   4.251 0.000106 ***
drugE - 2times == 0    11.723      1.443   8.122 2.29e-10 ***
drugD - 4times == 0     2.986      1.443   2.069 0.044316 *  
drugE - 4times == 0     8.573      1.443   5.939 3.84e-07 ***
drugE - drugD == 0      5.586      1.443   3.870 0.000348 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
(Univariate p values reported)

>   summary(cht, test = adjusted("Shaffer"))

	 Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: aov(formula = response ~ trt, data = cholesterol)

Linear Hypotheses:
                     Estimate Std. Error t value  p value    
2times - 1time == 0     3.443      1.443   2.385 0.042666 *  
4times - 1time == 0     6.593      1.443   4.568 0.000153 ***
drugD - 1time == 0      9.579      1.443   6.637 2.12e-07 ***
drugE - 1time == 0     15.166      1.443  10.507 1.08e-12 ***
4times - 2times == 0    3.150      1.443   2.182 0.042666 *  
drugD - 2times == 0     6.136      1.443   4.251 0.000317 ***
drugE - 2times == 0    11.723      1.443   8.122 1.38e-09 ***
drugD - 4times == 0     2.986      1.443   2.069 0.044316 *  
drugE - 4times == 0     8.573      1.443   5.939 1.54e-06 ***
drugE - drugD == 0      5.586      1.443   3.870 0.000697 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
(Adjusted p values reported -- Shaffer method)

>   summary(cht, test = adjusted("Westfall"))

	 Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: aov(formula = response ~ trt, data = cholesterol)

Linear Hypotheses:
                     Estimate Std. Error t value p value    
2times - 1time == 0     3.443      1.443   2.385  0.0420 *  
4times - 1time == 0     6.593      1.443   4.568   <0.01 ***
drugD - 1time == 0      9.579      1.443   6.637   <0.01 ***
drugE - 1time == 0     15.166      1.443  10.507   <0.01 ***
4times - 2times == 0    3.150      1.443   2.182  0.0420 *  
drugD - 2times == 0     6.136      1.443   4.251   <0.01 ***
drugE - 2times == 0    11.723      1.443   8.122   <0.01 ***
drugD - 4times == 0     2.986      1.443   2.069  0.0443 *  
drugE - 4times == 0     8.573      1.443   5.939   <0.01 ***
drugE - drugD == 0      5.586      1.443   3.870   <0.01 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
(Adjusted p values reported -- Westfall method)

> 
>   ### use only a subset of all pairwise hypotheses
>   K <- contrMat(table(cholesterol$trt), type="Tukey")
>   Ksub <- rbind(K[c(1,2,5),],
+                 "D - test" = c(-1, -1, -1, 3, 0),
+                 "E - test" = c(-1, -1, -1, 0, 3))
> 
>   ### reproduce results in Westfall et al. (1999, page 172)
>   amod <- aov(response ~ trt - 1, data = cholesterol)
>   summary(glht(amod, linfct = mcp(trt = Ksub[,5:1])), 
+           test = adjusted("Westfall"))

	 Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: User-defined Contrasts


Fit: aov(formula = response ~ trt - 1, data = cholesterol)

Linear Hypotheses:
                     Estimate Std. Error t value p value    
2times - 1time == 0    -5.586      1.443  -3.870  <0.001 ***
4times - 1time == 0    -8.573      1.443  -5.939  <0.001 ***
4times - 2times == 0   -2.986      1.443  -2.069  0.0443 *  
D - test == 0         -21.009      3.536  -5.942  <0.001 ***
E - test == 0         -31.338      3.536  -8.864  <0.001 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
(Adjusted p values reported -- Westfall method)

> 
> 
> 
> cleanEx(); nameEx("contrMat");
> ### * contrMat
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: contrMat
> ### Title: Contrast Matrices
> ### Aliases: contrMat
> ### Keywords: misc
> 
> ### ** Examples
> 
>  n <- c(10,20,30,40)
>  names(n) <- paste("group", 1:4, sep="")
>  contrMat(n)    # Dunnett is default

	 Multiple Comparisons of Means: Dunnett Contrasts

                group1 group2 group3 group4
group2 - group1     -1      1      0      0
group3 - group1     -1      0      1      0
group4 - group1     -1      0      0      1
>  contrMat(n, base = 2)  # use second level as baseline

	 Multiple Comparisons of Means: Dunnett Contrasts

                group1 group2 group3 group4
group1 - group2      1     -1      0      0
group3 - group2      0     -1      1      0
group4 - group2      0     -1      0      1
>  contrMat(n, type = "Tukey")

	 Multiple Comparisons of Means: Tukey Contrasts

                group1 group2 group3 group4
group2 - group1     -1      1      0      0
group3 - group1     -1      0      1      0
group4 - group1     -1      0      0      1
group3 - group2      0     -1      1      0
group4 - group2      0     -1      0      1
group4 - group3      0      0     -1      1
>  contrMat(n, type = "Sequen")

	 Multiple Comparisons of Means: Sequen Contrasts

                group1 group2 group3 group4
group2 - group1     -1      1      0      0
group3 - group2      0     -1      1      0
group4 - group3      0      0     -1      1
>  contrMat(n, type = "AVE")

	 Multiple Comparisons of Means: AVE Contrasts

     group1  group2  group3  group4
C 1  1.0000 -0.2222 -0.3333 -0.4444
C 2 -0.1250  1.0000 -0.3750 -0.5000
C 3 -0.1429 -0.2857  1.0000 -0.5714
C 4 -0.1667 -0.3333 -0.5000  1.0000
>  contrMat(n, type = "Changepoint")

	 Multiple Comparisons of Means: Changepoint Contrasts

     group1  group2  group3 group4
C 1 -1.0000  0.2222  0.3333 0.4444
C 2 -0.3333 -0.6667  0.4286 0.5714
C 3 -0.1667 -0.3333 -0.5000 1.0000
>  contrMat(n, type = "Williams")

	 Multiple Comparisons of Means: Williams Contrasts

    group1 group2 group3 group4
C 1     -1 0.0000 0.0000 1.0000
C 2     -1 0.0000 0.4286 0.5714
C 3     -1 0.2222 0.3333 0.4444
>  contrMat(n, type = "Marcus")

	 Multiple Comparisons of Means: Marcus Contrasts

     group1  group2  group3 group4
C 1 -1.0000  0.2222  0.3333 0.4444
C 2 -1.0000  0.0000  0.4286 0.5714
C 3 -0.3333 -0.6667  0.4286 0.5714
C 4 -1.0000  0.0000  0.0000 1.0000
C 5 -0.3333 -0.6667  0.0000 1.0000
C 6 -0.1667 -0.3333 -0.5000 1.0000
>  contrMat(n, type = "McDermott")

	 Multiple Comparisons of Means: McDermott Contrasts

     group1  group2 group3 group4
C 1 -1.0000  1.0000    0.0      0
C 2 -0.3333 -0.6667    1.0      0
C 3 -0.1667 -0.3333   -0.5      1
> 
> 
> 
> cleanEx(); nameEx("detergent");
> ### * detergent
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: detergent
> ### Title: Detergent Durability Data Set
> ### Aliases: detergent
> ### Keywords: datasets
> 
> ### ** Examples
> 
> 
>   ### set up two-way ANOVA without interactions
>   amod <- aov(plates ~ block + detergent, data = detergent)
> 
>   ### set up all-pair comparisons
>   dht <- glht(amod, linfct = mcp(detergent = "Tukey"))
> 
>   ### see Westfall et al. (1999, p. 190)
>   confint(dht)

	 Simultaneous Confidence Intervals for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: aov(formula = plates ~ block + detergent, data = detergent)

Estimated Quantile = 3.0646

Linear Hypotheses:
           Estimate lwr      upr     
B - A == 0  -2.1333  -4.7932   0.5266
C - A == 0   3.6000   0.9401   6.2599
D - A == 0   2.2000  -0.4599   4.8599
E - A == 0  -4.3333  -6.9932  -1.6734
C - B == 0   5.7333   3.0734   8.3932
D - B == 0   4.3333   1.6734   6.9932
E - B == 0  -2.2000  -4.8599   0.4599
D - C == 0  -1.4000  -4.0599   1.2599
E - C == 0  -7.9333 -10.5932  -5.2734
E - D == 0  -6.5333  -9.1932  -3.8734

95% family-wise confidence level
 

> 
>   ### see Westfall et al. (1999, p. 192)
>   summary(dht, test = univariate())

	 Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: aov(formula = plates ~ block + detergent, data = detergent)

Linear Hypotheses:
           Estimate Std. Error t value  p value    
B - A == 0  -2.1333     0.8679  -2.458 0.025762 *  
C - A == 0   3.6000     0.8679   4.148 0.000757 ***
D - A == 0   2.2000     0.8679   2.535 0.022075 *  
E - A == 0  -4.3333     0.8679  -4.993 0.000133 ***
C - B == 0   5.7333     0.8679   6.606 6.05e-06 ***
D - B == 0   4.3333     0.8679   4.993 0.000133 ***
E - B == 0  -2.2000     0.8679  -2.535 0.022075 *  
D - C == 0  -1.4000     0.8679  -1.613 0.126291    
E - C == 0  -7.9333     0.8679  -9.140 9.45e-08 ***
E - D == 0  -6.5333     0.8679  -7.527 1.21e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
(Univariate p values reported)

>   summary(dht, test = adjusted("Shaffer"))

	 Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: aov(formula = plates ~ block + detergent, data = detergent)

Linear Hypotheses:
           Estimate Std. Error t value  p value    
B - A == 0  -2.1333     0.8679  -2.458 0.051524 .  
C - A == 0   3.6000     0.8679   4.148 0.003028 ** 
D - A == 0   2.2000     0.8679   2.535 0.044149 *  
E - A == 0  -4.3333     0.8679  -4.993 0.000531 ***
C - B == 0   5.7333     0.8679   6.606 3.63e-05 ***
D - B == 0   4.3333     0.8679   4.993 0.000398 ***
E - B == 0  -2.2000     0.8679  -2.535 0.044149 *  
D - C == 0  -1.4000     0.8679  -1.613 0.126291    
E - C == 0  -7.9333     0.8679  -9.140 9.45e-07 ***
E - D == 0  -6.5333     0.8679  -7.527 7.26e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
(Adjusted p values reported -- Shaffer method)

>   summary(dht, test = adjusted("Westfall"))

	 Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: aov(formula = plates ~ block + detergent, data = detergent)

Linear Hypotheses:
           Estimate Std. Error t value p value    
B - A == 0  -2.1333     0.8679  -2.458   0.050 .  
C - A == 0   3.6000     0.8679   4.148   <0.01 ** 
D - A == 0   2.2000     0.8679   2.535   0.043 *  
E - A == 0  -4.3333     0.8679  -4.993   <0.01 ***
C - B == 0   5.7333     0.8679   6.606   <0.01 ***
D - B == 0   4.3333     0.8679   4.993   <0.01 ***
E - B == 0  -2.2000     0.8679  -2.535   0.043 *  
D - C == 0  -1.4000     0.8679  -1.613   0.126    
E - C == 0  -7.9333     0.8679  -9.140   <0.01 ***
E - D == 0  -6.5333     0.8679  -7.527   <0.01 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
(Adjusted p values reported -- Westfall method)

> 
> 
> 
> 
> cleanEx(); nameEx("glht");
> ### * glht
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: glht
> ### Title: General Linear Hypotheses
> ### Aliases: glht glht.matrix glht.character glht.expression glht.mcp mcp
> ### Keywords: htest
> 
> ### ** Examples
> 
> 
>   ### multiple linear model, swiss data
>   lmod <- lm(Fertility ~ ., data = swiss)
> 
>   ### test of H_0: all regression coefficients are zero 
>   ### (ignore intercept)
> 
>   ### define coefficients of linear function directly
>   K <- diag(length(coef(lmod)))[-1,]
>   rownames(K) <- names(coef(lmod))[-1]
>   K
                 [,1] [,2] [,3] [,4] [,5] [,6]
Agriculture         0    1    0    0    0    0
Examination         0    0    1    0    0    0
Education           0    0    0    1    0    0
Catholic            0    0    0    0    1    0
Infant.Mortality    0    0    0    0    0    1
> 
>   ### set up general linear hypothesis
>   glht(lmod, linfct = K)

	 General Linear Hypotheses

Linear Hypotheses:
                      Estimate
Agriculture == 0       -0.1721
Examination == 0       -0.2580
Education == 0         -0.8709
Catholic == 0           0.1041
Infant.Mortality == 0   1.0770

> 
>   ### alternatively, use a symbolic description 
>   ### instead of a matrix 
>   glht(lmod, linfct = c("Agriculture = 0",
+                         "Examination = 0",
+                         "Education = 0",
+                         "Catholic = 0",
+                         "Infant.Mortality = 0"))

	 General Linear Hypotheses

Linear Hypotheses:
                      Estimate
Agriculture == 0       -0.1721
Examination == 0       -0.2580
Education == 0         -0.8709
Catholic == 0           0.1041
Infant.Mortality == 0   1.0770

> 
>   ### multiple comparison procedures
>   ### set up a one-way ANOVA
>   amod <- aov(breaks ~ tension, data = warpbreaks)
> 
>   ### set up all-pair comparisons for factor `tension'
>   ### using a symbolic description (`type' argument 
>   ### to `contrMat()')
>   glht(amod, linfct = mcp(tension = "Tukey"))

	 General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Linear Hypotheses:
           Estimate
M - L == 0  -10.000
H - L == 0  -14.722
H - M == 0   -4.722

> 
>   ### alternatively, describe differences symbolically
>   glht(amod, linfct = mcp(tension = c("M - L = 0", 
+                                       "H - L = 0",
+                                       "H - M = 0")))

	 General Linear Hypotheses

Multiple Comparisons of Means: User-defined Contrasts


Linear Hypotheses:
           Estimate
M - L == 0  -10.000
H - L == 0  -14.722
H - M == 0   -4.722

> 
>   ### alternatively, define contrast matrix directly
>   contr <- rbind("M - L" = c(-1, 1, 0),
+                  "H - L" = c(-1, 0, 1), 
+                  "H - M" = c(0, -1, 1))
>   glht(amod, linfct = mcp(tension = contr))

	 General Linear Hypotheses

Multiple Comparisons of Means: User-defined Contrasts


Linear Hypotheses:
           Estimate
M - L == 0  -10.000
H - L == 0  -14.722
H - M == 0   -4.722

> 
>   ### alternatively, define linear function for coef(amod)
>   ### instead of contrasts for `tension'
>   ### (take model contrasts and intercept into account)
>   glht(amod, linfct = cbind(0, contr %*% contr.treatment(3)))

	 General Linear Hypotheses

Linear Hypotheses:
           Estimate
M - L == 0  -10.000
H - L == 0  -14.722
H - M == 0   -4.722

> 
> 
> 
> 
> cleanEx(); nameEx("litter");
> ### * litter
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: litter
> ### Title: Litter Weights Data Set
> ### Aliases: litter
> ### Keywords: datasets
> 
> ### ** Examples
> 
> 
>   ### fit ANCOVA model to data
>   amod <- aov(weight ~ dose + gesttime + number, data = litter)
> 
>   ### define matrix of linear hypotheses for `dose'
>   doselev <- as.integer(levels(litter$dose))
>   K <- rbind(contrMat(table(litter$dose), "Tukey"),
+              otrend = c(-1.5, -0.5, 0.5, 1.5),
+              atrend = doselev - mean(doselev),
+              ltrend = log(1:4) - mean(log(1:4)))
> 
>   ### set up multiple comparison object
>   Kht <- glht(amod, linfct = mcp(dose = K), alternative = "less")
> 
>   ### cf. Westfall (1997, Table 2)
>   summary(Kht, test = univariate())

	 Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: User-defined Contrasts


Fit: aov(formula = weight ~ dose + gesttime + number, data = litter)

Linear Hypotheses:
               Estimate Std. Error t value p value   
5 - 0 >= 0      -3.3524     1.2908  -2.597 0.00575 **
50 - 0 >= 0     -2.2909     1.3384  -1.712 0.04576 * 
500 - 0 >= 0    -2.6752     1.3343  -2.005 0.02448 * 
50 - 5 >= 0      1.0615     1.3973   0.760 0.77498   
500 - 5 >= 0     0.6772     1.3394   0.506 0.69260   
500 - 50 >= 0   -0.3844     1.4510  -0.265 0.39595   
otrend >= 0     -3.4821     2.0867  -1.669 0.04988 * 
atrend >= 0   -314.7324   408.9901  -0.770 0.22212   
ltrend >= 0     -1.9400     0.9616  -2.018 0.02379 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
(Univariate p values reported)

>   summary(Kht, test = adjusted("bonferroni"))

	 Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: User-defined Contrasts


Fit: aov(formula = weight ~ dose + gesttime + number, data = litter)

Linear Hypotheses:
               Estimate Std. Error t value p value  
5 - 0 >= 0      -3.3524     1.2908  -2.597  0.0518 .
50 - 0 >= 0     -2.2909     1.3384  -1.712  0.4118  
500 - 0 >= 0    -2.6752     1.3343  -2.005  0.2203  
50 - 5 >= 0      1.0615     1.3973   0.760  1.0000  
500 - 5 >= 0     0.6772     1.3394   0.506  1.0000  
500 - 50 >= 0   -0.3844     1.4510  -0.265  1.0000  
otrend >= 0     -3.4821     2.0867  -1.669  0.4490  
atrend >= 0   -314.7324   408.9901  -0.770  1.0000  
ltrend >= 0     -1.9400     0.9616  -2.018  0.2141  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
(Adjusted p values reported -- bonferroni method)

>   summary(Kht, test = adjusted("Shaffer"))

	 Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: User-defined Contrasts


Fit: aov(formula = weight ~ dose + gesttime + number, data = litter)

Linear Hypotheses:
               Estimate Std. Error t value p value  
5 - 0 >= 0      -3.3524     1.2908  -2.597  0.0518 .
50 - 0 >= 0     -2.2909     1.3384  -1.712  0.0915 .
500 - 0 >= 0    -2.6752     1.3343  -2.005  0.0734 .
50 - 5 >= 0      1.0615     1.3973   0.760  1.0000  
500 - 5 >= 0     0.6772     1.3394   0.506  1.0000  
500 - 50 >= 0   -0.3844     1.4510  -0.265  1.0000  
otrend >= 0     -3.4821     2.0867  -1.669  0.0998 .
atrend >= 0   -314.7324   408.9901  -0.770  0.4442  
ltrend >= 0     -1.9400     0.9616  -2.018  0.0518 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
(Adjusted p values reported -- Shaffer method)

>   summary(Kht, test = adjusted("Westfall"))

	 Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: User-defined Contrasts


Fit: aov(formula = weight ~ dose + gesttime + number, data = litter)

Linear Hypotheses:
               Estimate Std. Error t value p value  
5 - 0 >= 0      -3.3524     1.2908  -2.597  0.0320 *
50 - 0 >= 0     -2.2909     1.3384  -1.712  0.0893 .
500 - 0 >= 0    -2.6752     1.3343  -2.005  0.0647 .
50 - 5 >= 0      1.0615     1.3973   0.760  0.7750  
500 - 5 >= 0     0.6772     1.3394   0.506  0.7271  
500 - 50 >= 0   -0.3844     1.4510  -0.265  0.7271  
otrend >= 0     -3.4821     2.0867  -1.669  0.0917 .
atrend >= 0   -314.7324   408.9901  -0.770  0.3951  
ltrend >= 0     -1.9400     0.9616  -2.018  0.0459 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
(Adjusted p values reported -- Westfall method)

>   summary(Kht, test = adjusted("free"))

	 Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: User-defined Contrasts


Fit: aov(formula = weight ~ dose + gesttime + number, data = litter)

Linear Hypotheses:
               Estimate Std. Error t value p value  
5 - 0 >= 0      -3.3524     1.2908  -2.597   0.032 *
50 - 0 >= 0     -2.2909     1.3384  -1.712   0.203  
500 - 0 >= 0    -2.6752     1.3343  -2.005   0.119  
50 - 5 >= 0      1.0615     1.3973   0.760   1.000  
500 - 5 >= 0     0.6772     1.3394   0.506   0.999  
500 - 50 >= 0   -0.3844     1.4510  -0.265   0.891  
otrend >= 0     -3.4821     2.0867  -1.669   0.219  
atrend >= 0   -314.7324   408.9901  -0.770   0.662  
ltrend >= 0     -1.9400     0.9616  -2.018   0.115  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
(Adjusted p values reported)

> 
> 
> 
> 
> cleanEx(); nameEx("methods");
> ### * methods
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: glht-methods
> ### Title: Methods for General Linear Hypotheses
> ### Aliases: summary.glht confint.glht coef.glht vcov.glht univariate
> ###   adjusted Ftest Chisqtest
> ### Keywords: htest
> 
> ### ** Examples
> 
> 
>   ### set up a two-way ANOVA with interactions
>   amod <- aov(breaks ~ wool * tension, data = warpbreaks)
> 
>   ### set up all-pair comparisons for factor `tension'
>   wht <- glht(amod, linfct = mcp(tension = "Tukey"))
> 
>   ### 95% simultaneous confidence intervals
>   plot(print(confint(wht)))

	 Simultaneous Confidence Intervals for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: aov(formula = breaks ~ wool * tension, data = warpbreaks)

Estimated Quantile = 2.4188

Linear Hypotheses:
           Estimate lwr      upr     
M - L == 0 -10.0000 -18.8208  -1.1792
H - L == 0 -14.7222 -23.5430  -5.9014
H - M == 0  -4.7222 -13.5430   4.0986

95% family-wise confidence level
 

> 
>   ### the same (for balanced designs only)
>   TukeyHSD(amod, "tension")
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = breaks ~ wool * tension, data = warpbreaks)

$tension
          diff       lwr       upr     p adj
M-L -10.000000 -18.81965 -1.180353 0.0228554
H-L -14.722222 -23.54187 -5.902575 0.0005595
H-M  -4.722222 -13.54187  4.097425 0.4049442

> 
>   ### corresponding adjusted p values
>   summary(wht)

	 Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: aov(formula = breaks ~ wool * tension, data = warpbreaks)

Linear Hypotheses:
           Estimate Std. Error t value p value    
M - L == 0  -10.000      3.647  -2.742  0.0229 *  
H - L == 0  -14.722      3.647  -4.037  <0.001 ***
H - M == 0   -4.722      3.647  -1.295  0.4049    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
(Adjusted p values reported)

> 
>   ### confidence bands for a simple linear model, `cars' data
>   plot(cars, xlab = "Speed (mph)", ylab = "Stopping distance (ft)",
+        las = 1)
> 
>   ### fit linear model and add regression line to plot
>   lmod <- lm(dist ~ speed, data = cars)
>   abline(lmod)
> 
>   ### a grid of speeds
>   speeds <- seq(from = min(cars$speed), to = max(cars$speed), 
+                 length = 10)
> 
>   ### linear hypotheses: 10 selected points on the regression line != 0
>   K <- cbind(1, speeds)                                                        
> 
>   ### set up linear hypotheses
>   cht <- glht(lmod, linfct = K)
> 
>   ### confidence intervals, i.e., confidence bands, and add them plot
>   cci <- confint(cht)
>   lines(speeds, cci$confint[,"lwr"], col = "blue")
>   lines(speeds, cci$confint[,"upr"], col = "blue")
> 
>   ### simultaneous p values for parameters in a Cox model
>   if (require("survival") && require("MASS")) {
+       data("leuk", package = "MASS")
+       leuk.cox <- coxph(Surv(time) ~ ag + log(wbc), data = leuk)
+ 
+       ### set up linear hypotheses
+       lht <- glht(leuk.cox, linfct = diag(length(coef(leuk.cox))))
+ 
+       ### adjusted p values
+       print(summary(lht))
+   }
Loading required package: survival
Loading required package: splines
Loading required package: MASS

	 Simultaneous Tests for General Linear Hypotheses

Fit: coxph(formula = Surv(time) ~ ag + log(wbc), data = leuk)

Linear Hypotheses:
       Estimate Std. Error z value p value  
1 == 0  -1.0691     0.4293  -2.490  0.0253 *
2 == 0   0.3677     0.1360   2.703  0.0137 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
(Adjusted p values reported)

> 
> 
> 
> 
> cleanEx(); nameEx("recovery");
> ### * recovery
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: recovery
> ### Title: Recovery Time Data Set
> ### Aliases: recovery
> ### Keywords: datasets
> 
> ### ** Examples
> 
> 
>   ### set up one-way ANOVA
>   amod <- aov(minutes ~ blanket, data = recovery)
> 
>   ### set up multiple comparisons: one-sided Dunnett contrasts
>   rht <- glht(amod, linfct = mcp(blanket = "Dunnett"), 
+               alternative = "less")
> 
>   ### cf. Westfall et al. (1999, p. 80)
>   confint(rht, level = 0.9)

	 Simultaneous Confidence Intervals for General Linear Hypotheses

Multiple Comparisons of Means: Dunnett Contrasts


Fit: aov(formula = minutes ~ blanket, data = recovery)

Estimated Quantile = -1.8433

Linear Hypotheses:
             Estimate lwr      upr     
b1 - b0 >= 0 -2.13333     -Inf  0.82298
b2 - b0 >= 0 -7.46667     -Inf -4.51036
b3 - b0 >= 0 -1.66667     -Inf -0.03574

90% family-wise confidence level
 

> 
>   ### the same
>   rht <- glht(amod, linfct = mcp(blanket = c("b1 - b0 >= 0", 
+                                              "b2 - b0 >= 0", 
+                                              "b3 - b0 >= 0")))
>   confint(rht, level = 0.9)

	 Simultaneous Confidence Intervals for General Linear Hypotheses

Multiple Comparisons of Means: User-defined Contrasts


Fit: aov(formula = minutes ~ blanket, data = recovery)

Estimated Quantile = -1.8431

Linear Hypotheses:
             Estimate lwr      upr     
b1 - b0 >= 0 -2.13333     -Inf  0.82254
b2 - b0 >= 0 -7.46667     -Inf -4.51079
b3 - b0 >= 0 -1.66667     -Inf -0.03598

90% family-wise confidence level
 

> 
> 
> 
> 
> cleanEx(); nameEx("waste");
> ### * waste
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: waste
> ### Title: Industrial Waste Data Set
> ### Aliases: waste
> ### Keywords: datasets
> 
> ### ** Examples
> 
> 
>   ### set up two-way ANOVA with interactions
>   amod <- aov(waste ~ envir + temp + envir*temp, data=waste)
> 
>   ### set up linear hypotheses for all-pairs of both factors
>   wht <- glht(amod, linfct = mcp(temp = "Tukey", envir = "Tukey"))
> 
>   ### cf. Westfall et al. (1999, page 181)
>   summary(wht, test = adjusted("Shaffer"))

	 Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: aov(formula = waste ~ envir + temp + envir * temp, data = waste)

Linear Hypotheses:
                         Estimate Std. Error t value p value   
temp: low - high == 0     -2.0150     0.4847  -4.157 0.00590 **
temp: medium - high == 0  -2.2560     0.4847  -4.654 0.00405 **
temp: medium - low == 0   -0.2410     0.4847  -0.497 1.00000   
envir: env2 - env1 == 0    1.3833     0.6258   2.211 0.21508   
envir: env3 - env1 == 0    1.6850     0.6258   2.693 0.08351 . 
envir: env4 - env1 == 0    2.0183     0.6258   3.225 0.03964 * 
envir: env5 - env1 == 0    2.7533     0.6258   4.400 0.00569 **
envir: env3 - env2 == 0    0.3017     0.6258   0.482 1.00000   
envir: env4 - env2 == 0    0.6350     0.6258   1.015 1.00000   
envir: env5 - env2 == 0    1.3700     0.6258   2.189 0.31361   
envir: env4 - env3 == 0    0.3333     0.6258   0.533 1.00000   
envir: env5 - env3 == 0    1.0683     0.6258   1.707 0.43356   
envir: env5 - env4 == 0    0.7350     0.6258   1.175 0.77546   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
(Adjusted p values reported -- Shaffer method)

> 
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", pos = 'CheckExEnv'),"\n")
Time elapsed:  51.711 0.084 52.115 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')